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Work 51 (2015) 373–381 DOI 10.3233/WOR-141980 IOS Press
Review Article
A systematic review of studies identifying predictors of poor return to work outcomes following workplace injury Tamara D. Streeta,∗ and Sarah J. Laceyb a
The Wesley Research Institute, Queensland, Australia Centre for Accident Research and Road Safety, Queensland University of Technology, Brisbane, Queensland, Australia
b
Received 12 August 2013 Accepted 15 April 2014
Abstract. BACKGROUND: Injuries occurring in the workplace can have serious implications for the health of the individual, the productivity of the employer and the overall economic community. OBJECTIVE: The objective of this paper is to increase the current state of understanding of individual demographic and psychosocial characteristics associated with extended absenteeism from the workforce due to a workplace injury. METHODS: Studies included in this systematic literature review tracked participants’ return to work status over a minimum of three months, identified either demographic, psychosocial or general injury predictors of poor return to work outcomes and included a heterogeneous sample of workplace injuries. RESULTS: Identified predictors of poor return to work outcomes included older age, female gender, divorced marital status, two or more dependent family members, lower education levels, employment variables associated with reduced labour market desirability, severity or sensitive injury locations, negative attitudes and outcome perceptions of the participant. CONCLUSIONS: There is a need for clear and consistent definition and measurement of return to work outcomes and a holistic theoretical model integrating injury, psychosocial and demographic predictors of return to work. Through greater understanding of the nature of factors affecting return to work, improved outcomes could be achieved. Keywords: Systematic review, return to work, occupational rehabilitation, workplace injury, predictors of return to work, injury outcome
1. Introduction Workplace injuries can significantly affect the wellbeing of the injured employee, organisational productivity and the wider economic community. In Australia, ∗ Corresponding author: Tamara D. Street, Wesley Research Institute, PO Box 499, Toowong, QLD 4066, Australia. Tel.: +61 7 3721 1706; Fax: +61 7 3721 1590; E-mail: tstreet@wesleyresearch. com.au.
it was estimated that in 2005–6 the cost of work-related injuries amounted to $57.5 billion dollars, a sum equal to 5.9% of Gross Domestic Product (GDP) when all factors including work related absenteeism, reduced worker functionality, government provided health care and welfare payments were accounted for [1]. Despite the growing interest in rehabilitation interventions in response to workplace injury costs, research in this area remains limited and the complex interaction of factors that contribute to long term absenteeism is poorly understood.
c 2015 – IOS Press and the authors. All rights reserved 1051-9815/15/$35.00
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Historically, the concept of return to work outcomes has been inadequately defined in the literature, inconsistently measured and has not been accurately applied to a theoretical model [14,16]. Such limitations may be attributable to the largely unavoidable retrospective nature of return to work records and the often incomplete records available. The paucity in the literature has lead to many studies and subsequent interventions focusing on a single variable such as injury severity as a predictor of return to work outcomes [11]. While it stands to reason that injury location and severity may be a leading determinant of prolonged absenteeism and rehabilitation requirements (such as in the case of musculoskeletal, brain and burn injuries), such connections are well documented and few studies have incorporated other variables such as work, demographic and psychosocial factors associated with the injured individual [8]. The authors were unable to identify an existing holistic theoretical model that included the complex interaction of injury, work, demographic and psychosocial factors. In the absence of a clear theoretical model that encompasses the combined risk factors of long term absenteeism, the ability to develop, dissect and evaluate effective rehabilitation interventions is limited and replication of results becomes less likely. Identifying and modelling the relationship between these factors will prove valuable in informing the development of rehabilitation and support services for ‘at risk’ sufferers of workplace injury and reduce potentially long term absenteeism rates for employers. In consideration of a theoretical model of predictors of return to work, Lilley, Davie, Ameratunga and Derrett [10] assessed a combination of injury, demographic and psychosocial predictors of return to work and anecdotally suggested that the results should be further validated and applied to Engel’s [3] Biopsychosocial model (BPS) of health. According to this model, biological, psychological and psychosocial factors are represented in an equilateral ven diagram with the centre overlapping interaction indicating the cumulative overall health of an individual. However, Lilley et al. [10] and Mills [11] both acknowledge that researchers have intuitively agreed that injury factors such as severity and location of the injury will play a larger role in predicting poor return to work outcomes. Thus, while the existing biopsychosocial model may be used to guide future modelling of predictors of return to work, the equal variance, unquantifiable effect of individual variables, and absence of injury factors allows limited application of the model to real world rehabilitation prediction outcomes.
In addition, a recent study by Lainse, Lecomte and Corbiere [8] on biopsychosocial predictors in patients with musculoskeletal injuries found that psychosocial variables such as the importance of work to the individual and work support were predominant in predicting return to work outcomes. Although the article did not propose a specific model of the interaction between known predictive variables, it did significantly advance the understanding and importance of biopsychosocial factors by highlighting the importance of lesser measured variables such as post traumatic stress disorder (PTSD) symptomology and perceived importance of work. This finding further confirms the limited understanding in the present literature and the need for consolidation and validation of existing results. It is important to understand the factors associated with predicting long term absenteeism and non return to employment as a result of workplace injury in order to allow individuals at high risk to be identified and offered proactive rehabilitation programs and support services. Hence, the purpose of this systematic literature review was to identify predictors of poor return to work outcomes (as measured by long term absenteeism) in order to inform the future development of a holistic theoretical model and rehabilitation programs. The report details the process and findings of the review on demographic and psychosocial predictors of poor return to work outcomes following workplace injury. It further presents the current state of understanding of individual demographic and psychosocial characteristics associated with extended absenteeism. Herein, a return to work outcome was defined as resumption of a minimum of part time employment following a workplace injury related absenteeism of three months or greater.
2. Methods 2.1. Literature search A comprehensive literature search was conducted of scientific databases including Cochrane, EBSCOhost (CINAHL, Medline Complete, Humanities Source and PsychInfo), ProQuest and Science Direct using the terms: “predict* return to work” or “return to work” and “workplace injury”. The use of broad search terms was deliberately employed in order to identify all potential studies related to the subject matter. However, results were limited to publications entirely in English and which appeared in peer-reviewed journals between
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32 records identified through reference lists
6,389 titles excluded
485 abstracts screened
420 abstracts excluded
65 full-text articles assessed for eligibility
56 full-text articles excluded
9 studies included in qualitative synthesis
Screening
6,874 titles screened
Eligibility
6,842 records identified through databases
Included
Identification
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
Fig. 1. Summary procedure of the selection of studies and exclusion of studies based on predetermined inclusion criteria.
January 1990 and November 2012. Additional articles were sourced from the reference lists of the returned results and all potentially relevant studies were reviewed for suitability of inclusion within this study. The review focused on literature that included retrospective and longitudinal studies of injured workers. To be eligible for inclusion in the systematic literature review, a study must have satisfied a series of predefined criteria, namely: (a) tracked participants return to work status over a minimum of three months; (b) identified predictors of poor return to work outcomes; and (c) included a heterogeneous sample of workplace injuries. Studies that recorded only single injury cohorts such as brain or musculoskeletal injuries and only injury related predictors of return to work (e.g. pain ratings) were excluded due to their limited applicability to the general workforce. Brain and trauma injury studies were also excluded as the enduring nature of such injuries was considered likely to confound demographic and psychosocial predictors of return to work. Finally, studies were also excluded based on only including qualitative measures such as focus group data or subjective measures such as cessation of welfare payments. Although cessation of welfare payments has been interpreted by some authors as an indication of return to work status, the reviewers deemed such assumptions to be a potentially misleading measure of
return to work status as welfare payments may have been discontinued due to any number of reasons. The preliminary database search returned 6,842 results. An initial screening of the article titles resulted in 6,389 studies being excluded from the review. A review of abstracts of the remaining 453 articles resulted in an additional 420 studies being excluded. From the residual 33 studies, a search of their reference lists revealed an additional 32 titles that potentially met the selection criteria for inclusion. A full text evaluation of the final 65 studies resulted in 56 being excluded based on application of the defined selection criteria. The remaining nine studies were analysed and considered suitable for inclusion in this systematic review. Figure 1 illustrates the search and review processes involved in selecting studies. Each process was performed by two of four independent researchers and any discrepancies were resolved through discussion and review of the research question and selection criteria. 2.2. Methodological quality assessment The quality of each study in this analysis was assessed independently by two reviewers based on Hoesfsmit, Houkes and Nijkuis’ [6] modified version of the Cochrane review criteria with weighted scores (see Table 1). Final scores were determined by collaboration
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T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury Table 1 Criteria for evaluating the quality of empirical research
Criteria Design
Study population
Measurement Instruments
Data analysis
Outcomes
Evaluation Randomized Cluster randomised control trial trial (++) (++) 100 respondents (yes: ++ / no: - -) 2 times at least 3 months apart (++) Validity and reliability of instruments described or logically no description (yes: ++ / no: - -) Hierarchical re- Multiple regression gression or MANOVA (++) (+) Outcomes described (yes: ++ / no: - -)
Controlled trial or quasi-experiment (+)
Longitudinal Cross sectional Case/case study study series study (+) (-) (- -) Heterogeneous: respondents of different ages, gender and/or educational levels (yes: ++ / no: - -) 2 times at less than 3 months 1 data collection time (+) (-) Instruments are likely to be accepted in the profession under concern (yes: ++ / no: - -) Correlation, Survival analysis Logistical regres- Regression Chi-square or sion or Descrip- analysis or T-test ANOVA tive statistics (+) (-) (-) (-) Outcomes match study aims (yes: ++ / no: - -)
Note: - - = minus two, insufficient; - = minus one, sufficient; + = one, good; ++ = two, very good. A criterion is also ranked with a – where it could not be identified based on the text in the article. Table 2 Methodological quality of the included studies Study Cheadle et al. [2] He et al. [5] Kong et al. [7] Mills [11] Foreman et al. [4] Xu et al. [17] Li-Tsang et al. [9] Park [13] Tate [15]
Design + + + + + + + + +
Study population ++ ++ ++ ++ + + + ++ +
Follow up time ++ ++ ++ ++ ++ ++ -
Instruments ++ + + + + ++ ++ + +
Data analysis + + ++ + + + -
Outcomes ++ ++ ++ ++ ++ ++ ++ ++ ++
Rating Good Good Good Good Moderate Moderate Moderate Moderate Moderate
Note: Overall methodological quality rating of quantitative studies: - - (insufficient), - (poor), + (moderate), ++ (good).
of the independent reviews and were found to be uniform in the majority of cases. Table 2 outlines the reviewers’ final rating of the methodological quality of included studies. No studies were excluded based on the outcome of this process. Overall, the quantitative studies selected for review were assessed to have moderate to high quality methods associated with their design, study population, follow-up time, instruments and data analyses. Four studies [2,5,7,11] were considered to be of higher quality than the others due to their large, heterogeneous samples and sufficient follow-up time. In addition, these four studies generally employed superior data analyses techniques which added additional credibility to their findings. The quality assessment process undertaken also highlighted a number of limitations to existing research strategies. As noted by Cheadle et al. [2], Mills [11] and Tate [15], whilst the database extraction instrument used in all retrospective studies was rated as moder-
ate due to its general acceptance by the professional field, it provides limited value in assessing specific return to work related variables and may also contain undetectable administrative errors. Furthermore, the high rates of return to work outcomes observed by the reviewed studies (ranging from 50% [15] to 92% [5]) might also confound the assessment of predictors of poor return to work. 3. Results A total of 65 full journal articles were examined for eligibility and nine articles were included in the review. Appendix A illustrates the characteristics and predictors of poor return to work outcomes extracted from the included studies. 3.1. Study design and instruments Six of the nine studies [2,5,7,11,13,15] employed a retrospective cohort design whereby the researchers
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
extracted information from historical databases – including workers compensation claim records, employer records and medical files. Due to the limitations of retrospectively collected information, the majority of these studies examined only demographic and injury related predictors of return to work outcomes. The one exception was He et al. [5] who included a measure of psychosocial predictors of return to work based on primary sourced post-injury data collection. The remaining three studies [4,9,17] were prospective in nature, following participants longitudinally, and reported solely on psychosocial predictors of return to work. In these studies, the employed instruments of assessment varied greatly. While Foreman and Murphy [4] used semi-structured interviews based on expectancy theory, Xu et al. [17] employed a number of standard psychological measures and Li-Tsang et al. [9] used a combination of both (refer Appendix A). The number of participants in the included studies ranged from 32 to 28,473 and participant follow-up time also varied substantially from three months to in excess of three years. 3.2. Demographic predictors 3.2.1. Age Older age was found to be associated with poor return to work outcomes. Five of the studies [2,5,7,13, 15] in this review reported significant demographic predictors of poor return to work outcomes. All of these studies identified older age ( 46 years) as an important variable. It was aptly noted by Cheadle et al. [2] that this observed age effect was most likely due to both the reduced ability of older workers to recover from injury and the reduced likelihood of finding employment once they have recovered. 3.2.2. Gender Three studies assessed gender with two, namely Cheadle et al. [2] and Park [13], finding female gender was associated with poor return to work outcomes. In addition, a third study, Mills [11] reported an observed trend in female participants predicting poor return to work outcomes but was unable to show statistical significance for this finding. The authors of this literature review hypothesise that the traditional caregiver role of women in the home may impede injury recovery time, thus accounting for the lower return to work rates within this group. 3.2.3. Marital status Cheadle et al. [2] and He et al. [5] assessed marital status in connection with return to work outcomes.
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Although it was reported by Cheadle et al. [2] that divorced marital status was a stable predictor of poor return to work outcomes across the sample of 28,473 examined cases, the effect size of this finding was small. In contrast, He and colleagues [5] reported no significant correlation between marital status and poor return to work outcomes. However, it was noted that the limited sample size of this study (n = 323) may have influenced the lack of finding related to this variable. Commensurate with the conjecture that female gender is associated with poor return to work outcomes, it follows that divorcees with limited support in the home may similarly experience extended injury recovery times and therefore take longer to return to the workforce, if at all. 3.2.4. Dependent family members Of the nine studies that satisfied the criteria for inclusion, only He et al. [5] measured the association between number of dependent family members and return to work. It was reported that a higher number of dependents (n 2) was associated with a significantly longer duration of workforce absenteeism, but did not influence the final return to work outcome. In this case, the reviewers consider the definition of a higher number of dependent family members as being greater than or equal to two to reflect the cultural context of the Chinese society, from which participants were drawn, and may not be generally applicable across developed nations where higher numbers of dependents are common. 3.2.5. Education Four of the nine studies [5,7,13,15] found lower education level to be associated with poor return to work outcomes. Kong et al. [7] asserted that workers with high school or college education are 2.5 times more likely to return to work than those of lower education levels, whilst Park [13] surmised that injured workers with lower education levels may be less competitive in the labour market, especially where competition is high. This finding is also consistent with general education and employment trends across the Organisation for Economic Co-operation and Development (OECD) countries. For example, the OECD Employment Outlook 2012 [12] revealed that, in Australia, unemployment levels for individuals without an upper secondary education was 6.2% compared with 3.6% for those with an upper secondary school education and 2.8% with tertiary education. Thus, lower education level was found to be an important variable in predicting both general employment and poor return to work outcomes.
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3.2.6. Employment variables Employment variables were examined by three reviewed studies [2,5,15]. Within these reports, manual job descriptions [5], lower pre-injury wages and less time with employer [15], or employment in a firm size of less than 50 employees [2] were all found to be associated with poor return to work outcomes. In exploring these findings the reviewers consider it likely that manual job positions could, in comparison to white collar positions, result in injuries that demand greater recovery periods prior to returning to work. For example, injuries that occur in a manual occupation are likely to be of a higher physical severity and the associated level of functional fitness required to return to the job is also likely to be higher in most cases. In addition, these results are consistent with that of lower education levels (often associated with manual job descriptions) in predicting poor return to work outcomes. This finding may also be attributable to the job specific skill base in manual job descriptions resulting in limited transferability and a less competitive position when returning to the labour market. 3.3. Injury predictors Predictors of poor return to work outcomes related to the injury type and severity were reported by six of the nine studies [2,5,7,11,13,15]. The majority of these studies [5,7,13,15] reported higher injury severity as the most significant injury predictor of poor return to work outcomes. Furthermore, a specific diagnosis of: carpal tunnel syndrome [2]; back or neck injuries [2, 15] were also found to be predictive of adverse return to work outcomes. Mills [11] further suggested that the mechanism of injury (including lifting, muscular stress, sitting and repetitive movement) was also predictive of poor return to work outcomes. Thus, while this systematic literature review excluded studies that focused on a specific injury (such as brain and back injuries) as the findings could not be generalised across the workforce, it stands to reason that increased injury severity or sensitivity of the location of the injury would negatively influence recovery time and therefore be predictive of poor return to work outcomes. 3.4. Psychosocial predictors Of the five studies that assessed psychosocial predictors of poor return to work outcomes [4,5,7,9, 17] both Li-Tsang et al. [9] and Xu et al. [17] employed a number of standardised psychological mea-
sures and found only the C-LASER subscale of precontemplation (based on the Stages of Change theory) to be a significant predictor. This subscale assesses an individual’s readiness to change, and in the context of assessing return to work, resume employment. For example, Xu et al. [17] found that higher baseline scores on the C-LASER pre-contemplation subscale (reflecting reduced readiness to return to work) were significant in predicting continued absenteeism at the three month follow-up assessment. Similarly, it was reported that psychosocial measures of: poor valance and lower expected outcomes [4]; poor selfperceived health status and worrying about recurrence of injury [5]; and poor personal or family attitudes regarding return to work [7] predicted poor return to work outcomes. The common denominator between psychosocial predictors of poor return to work outcomes appears to be one of negative attitudes and perception prior to re-entry into the workforce. For example, the pre-contemplation Stage of Change represents an inability to acknowledge that dysfunctional behaviours are problematic. The other psychosocial predictors identified in this review mirror the notion of negative thought patterns and behaviours impeding return to work.
4. Discussion This systematic review examined predictors of poor return to work outcomes following workplace injury in general workplace injury populations. It was found that a number of demographic characteristics pertaining to older age, female gender, divorced marital status, two or more dependent family members and limited labour market competitiveness are predictive of poor return to work outcomes. In addition, injury and psychosocial predictor variables included injury severity, injury location and psychosocial assessments of negative attitudes and poor expectancy outcomes. Notably, the present review, and in particular the sporadic measurement of variables across studies, also highlights the lack of consensus in defining and measuring return to work outcomes. Comparison and assessment of the external validity of each predictive variable was further made difficult by the heterogeneity of study designs and narrow scope of retrospective data reviewed by most cases. For example, whereas Laisne and colleagues [8] supported the inclusion of: psychological factors such as symptoms of anxiety, depression and PTSD; and social factors such as work
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
importance and work support, in a model of predicting return to work outcomes, the homogeneous sample of musculoskeletal injuries raises the potential of limited application of the results across other injury categories and was therefore excluded from the present literature review. Regrettably, six of the nine studies reviewed in detail [2,5,7,11,13,15] relied solely on retrospective case file information not specifically tailored to assess return to work outcomes. Although this limitation is a reflection of practical difficulties faced by researchers in collecting data, especially as workplace injuries cannot be foreseen, it highlights a significant challenge in the field. As aptly noted by Young [18], failure to assess return to work outcomes in a manner that is both consistent and meaningful to stakeholders will result in diminished capacity to accurately develop rehabilitation interventions capable of influencing change. Hence, future research should involve a clear and consistent definition of return to work outcomes and the ability to tailor study designs to meet such definitions. The application of a holistic theoretical model would also enable more meaningful comparison of results and a clearer understanding and measurement of the value of predictor variables. Such improvements in research design would further enable more reliable and confident conclusions to be drawn by future reviewers. Finally, by improving the research methodology in this field, a greater understanding of the nature of factors that directly affect negative return to work outcomes may result in improved outcomes for the individual, employer and society as a whole. Specifically, greater understanding and consensus on an underpinning theoretical model in this area may facilitate early screening of injured workers in order to identify individuals with known risk factors associated with poor return to work outcomes. Moreover, rehabilitation programs and services could be tailored to address the needs of participants who exhibit such risk factors (e.g. older age, poor labour market competitiveness and low expectancy outcomes). It is therefore surmised that proactively identifying, theoretical modelling of, and providing additional tailored assistance to injured workers who are at a higher risk of poor recovery may enhance return to work outcomes.
[2]
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Cheadle, A., Franklin, G., Wolfhagen, C., Savarino, J., Liu, P.Y., Salley, C. & Weaver, M. Factors influencing the duration of work-related disability: A population based study of Washington state workers’ compensation. American Journal of Public Health. 1994; 84(2): 190-196. Engel, G.L. The need for a new medical model: A challenge for biomedicine. Science. 1977; 196: 129-136. Foreman, P. & Murphy, G. Work values and expectancies in occupational rehabilitation: The role of cognitive variables in the return-to-work process. Journal of Rehabilitation. 1996; 62(3): 44-48. He, Y., Hu, J., Yu, I.T., Gu, W., & Liang, Y. Determinants of return to work after occupational injury. Journal of Occupational Rehabilitation. 2010; 20(3): 378-386. Hoefsmit, N., Houkes, I. & Nijhuis, F.N. Intervention Characteristics that Facilitate Return to Work After Sickness Absence: A Systematic Literature Review. Journal of Occupational Rehabilitation. 2012; 22(4): 462-477. Kong, W., Tang, D., Luo, X., Yu, I.T., Liang, Y. & He, Y. Prediction of return to work outcomes under an injured worker case management program. Journal of Occupational Rehabilitation. 2012; 22(2): 230-240. Laisne, F., Lecomte, C. & Corbiere, M. Biopsychosocial determinants of work outcomes of workers with occupational injuries receiving compensation: A prospective study. Work. 2013; 44: 117-132. Li-Tsang, C.W., Chan, H.H., Lam, C.S., Lo-Hui, K.Y. & Chan, C.C. Psychosocial aspects of injured workers’ returning to work (RTW) in Hong Kong. Journal of Occupational Rehabilitation. 2007; 17(4): 727-742. Lilley, R., Davie, G., Ameratunga, S. & Derrett, S. Factors predicting work status 3 months after injury: Results from the Prospective Outcomes of Injury Study. BMJ Open. 2012; 2: 1-11. Mills, R. Predicting failure to return to work. Internal Medicine Journal. 2012; 42(8): 924-927. OECD. OECD Employment Outlook 2012. OECD Publishing. 2012. Available from: http://www.upf.edu/materials/bib/ docs/3334/employ/employ12.pdf. Park, S.K. Associations of demographic and injury-related factors with return to work among job-injured workers with disabilities in South Korea. Journal of Rehabilitation Medicine. 2012; 44(5), 473-476. Pransky, G., Gatchel, R., Linton, S.J. & Loisel, P. Improving return to work research. Journal of Occupational Rehabilation. 2005; 15(4): 453-457. Tate, D.G. Workers’ disability and return to work. American Journal of Physical Medicine & Rehabilitation. 1992; 71(2): 92-96. Wasiak, R., Young, A.E., Roessler, R.T., McPherson, K.M., van Poppell, M.N.M., & Anema, J.R. Measuring Return to Work. Journal of Occupational Rehabilitation. 2007; 17(4): 766-781. Xu, Y., Chan, C.C.H., Lam, C.S., Li-Tsang, C.W.P., Lo-Hui, K.Y.L., & Gatchel, R.J. Rehabilitation of injured workers with chronic pain: A stage of change phenomenon. Journal of Occupational Rehabilitation. 2007; 17(4): 727-742. Young, A.E. A developmental conceptulization of return to work. Journal of Occupational Rehabilitation. 2005; 15: 557568.
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Appendix A. Description of studies and predictors of poor return to work Author year country Cheadle et al. [2] USA
Design study population
Instrument
Follow up time
RTW status
Predictors of poor RTW
Retrospective cohort study N = 28,473 Employees of a large stateowned locomotive company Women 55 years, Men 60 years
– Computerised claim data
Variable M = 35.4 months
17.5% 6 months NRTW 12% 1 year NRTW 7.5% 2 years NRTW
Demographic predictors: – – – – – –
Older age Female gender Divorced marital status Construction and agricultural work Firm size < 50 Higher county unemployment rates
Injury predictors: – Diagnosis of carpal tunnel or back/ neck sprain – Hospitalisation within the first 28 days – Semi-structured Foreman et Prospective cohort study interview based al. [4] N = 32 on expectancy Australia Federal public sector employees theory off work for at least 4 months Age not specified – Company He et al. [5] Retrospective cohort study archival China N = 323 documents Injured workers treated at Work Injury Rehabilitation Centre, Guangdong. Women 55 years, Men 60 years
6 months
7 months
34.4% RTW full time 34.4% RTW part time 31.2% NRTW
Psychosocial predictors:
92% RTW 8% NRTW
Demographic predictors:
– Poor valance and expectancy
– Older age – Lower education level – Higher number of dependent family members – Technical title – Monthly salary pre-injury – If the severity had been rated by the local authority Injury predictors: – Injury locus – Injury severity – Pain in the injury locus Psychosocial predictors: – Self-perceived health status – Worrying about re-injury or RTW – Satisfaction with pre-injury job
Kong et al. [7] China
– Company Retrospective cohort study archival N = 523 documents Injured workers treated at Work Injury Rehabilitation Centre, Guangdong. Women 55 years, Men 60 years
3–8 months
77.9% RTW 22.1% NRTW
Demographic predictors: – – – –
Older age Lower education level Low skill qualification No computer skill training
Injury predictors: – Higher injury severity Psychosocial predictors: – – – –
Li-Tsang et al. [9] Hong Kong
Prospective cohort study N = 75 From a local workers’ health centre who had previously participated in conventional rehabilitation services. Excluded severe brain and spinal injuries Cluster 1: C-LASER scores of Contemplation and Action Cluster 2: C-LASER scores of Pre-contemplation Age 20–65 years
– Interview – Spinal Function Sort (SFS) – Loma Linda University Medical Centre Activity Sort (LLUMC) – Chinese Lam’s Assessment of Stages of Employment Readiness (C-LASER) – Short Form 36 (SF-36)
T1: Baseline T2: Post training session T3: Post training and RTW program *Specific timeline unclear
T1: Cluster 1 65.2% RTW Cluster 2 48.1% RTW T2: Cluster 1 73.7% RTW Cluster 2 46.4% RTW T3: Cluster 1 57.4% RTW Cluster 2 42.9% RTW
Poor family attitude to RTW Poor personal desire to RTW No psychological counselling Non participation in disability adjustment activity
Psychosocial predictors: – LASER factor sub-scores of precontemplation
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
Author year Design study population Instrument country – Worker’s comMills [11] Retrospective cohort study pensation claim Australia N = 9,048 records lodged Workers compensation claim 1 March to records 30 June, 2007 Age not specified Group A: n = 1,377 NSW Self insurer in the Retail sector Group B: n = 7,671 VIC State workers’ compensation authority (WorkSafe VIC) – Interview Park [13] Retrospective cohort study – Korean Workers’ South N = 13,078 Compensation Korea Injured workers registered in and 2005 as having a permanent disWelfare Services ability (KCOMWEL) Age 20–55 years insurance claim 90.1% male and telephone interview data – Korea Employment Information Services (KEIS) employment data Tate [15] USA
– Personal medical Retrospective cohort study files N = 200 Active working compensation claims January – June 1986 Blue collar workers at an automobile manufacturer, Michigan
Follow up time
RTW status
Predictors of poor RTW
3 months or until RTW
Group A: Days to RTW or 3 months Median = 25 days, SD = 55.5 days Group B: Days to RTW or 3 months Median = 76 days, SD = 52.8 days
Injury predictors:
37 months
47.7% RTW 18% RTW (new firm) 1.7% RTW (self employed) 32.2% NRTW
Demographic predictors:
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– Mechanisms of injury of lifting, muscular stress (no object), and posture / steeping / kneeling / sitting / repetitive movement
– Female gender – Older age – Lower education level Injury predictors: – High disability category
12 months
50% RTW 50% NRTW
Demographic predictors: – – – –
Older age Lower education Lower pre-injury wages Less years of employment at company
Injury predictors: – Higher injury severity – Back and neck injuries
Xu et al. [17] Hong Kong
– Chinese Lam’s Prospective cohort study 3 months post RTW 65.7% RTW Assessment of N = 67 program 34.3 NRTW Stages of Injured workers who particiEmployment pated in a 6 week RTW program Readiness Age 20–60 years (LASER) – VALPAR #19 (subset of Valpar Component Work Sample (VCWS)) – DEXTER Hand Evaluation System – Loma Linda University Medical Center (LLUMC) Activities Sort – Chinese version of the StateTrait Anxiety Inventory – Short Form 36 (SF-36)
Note. RTW = Return to work, NRTW = Non return to work.
Psychosocial predictors: – C-LASER factor sub-scores of precontemplation
Work 51 (2015) 373–381 DOI 10.3233/WOR-141980 IOS Press
Review Article
A systematic review of studies identifying predictors of poor return to work outcomes following workplace injury Tamara D. Streeta,∗ and Sarah J. Laceyb a
The Wesley Research Institute, Queensland, Australia Centre for Accident Research and Road Safety, Queensland University of Technology, Brisbane, Queensland, Australia
b
Received 12 August 2013 Accepted 15 April 2014
Abstract. BACKGROUND: Injuries occurring in the workplace can have serious implications for the health of the individual, the productivity of the employer and the overall economic community. OBJECTIVE: The objective of this paper is to increase the current state of understanding of individual demographic and psychosocial characteristics associated with extended absenteeism from the workforce due to a workplace injury. METHODS: Studies included in this systematic literature review tracked participants’ return to work status over a minimum of three months, identified either demographic, psychosocial or general injury predictors of poor return to work outcomes and included a heterogeneous sample of workplace injuries. RESULTS: Identified predictors of poor return to work outcomes included older age, female gender, divorced marital status, two or more dependent family members, lower education levels, employment variables associated with reduced labour market desirability, severity or sensitive injury locations, negative attitudes and outcome perceptions of the participant. CONCLUSIONS: There is a need for clear and consistent definition and measurement of return to work outcomes and a holistic theoretical model integrating injury, psychosocial and demographic predictors of return to work. Through greater understanding of the nature of factors affecting return to work, improved outcomes could be achieved. Keywords: Systematic review, return to work, occupational rehabilitation, workplace injury, predictors of return to work, injury outcome
1. Introduction Workplace injuries can significantly affect the wellbeing of the injured employee, organisational productivity and the wider economic community. In Australia, ∗ Corresponding author: Tamara D. Street, Wesley Research Institute, PO Box 499, Toowong, QLD 4066, Australia. Tel.: +61 7 3721 1706; Fax: +61 7 3721 1590; E-mail: tstreet@wesleyresearch. com.au.
it was estimated that in 2005–6 the cost of work-related injuries amounted to $57.5 billion dollars, a sum equal to 5.9% of Gross Domestic Product (GDP) when all factors including work related absenteeism, reduced worker functionality, government provided health care and welfare payments were accounted for [1]. Despite the growing interest in rehabilitation interventions in response to workplace injury costs, research in this area remains limited and the complex interaction of factors that contribute to long term absenteeism is poorly understood.
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Historically, the concept of return to work outcomes has been inadequately defined in the literature, inconsistently measured and has not been accurately applied to a theoretical model [14,16]. Such limitations may be attributable to the largely unavoidable retrospective nature of return to work records and the often incomplete records available. The paucity in the literature has lead to many studies and subsequent interventions focusing on a single variable such as injury severity as a predictor of return to work outcomes [11]. While it stands to reason that injury location and severity may be a leading determinant of prolonged absenteeism and rehabilitation requirements (such as in the case of musculoskeletal, brain and burn injuries), such connections are well documented and few studies have incorporated other variables such as work, demographic and psychosocial factors associated with the injured individual [8]. The authors were unable to identify an existing holistic theoretical model that included the complex interaction of injury, work, demographic and psychosocial factors. In the absence of a clear theoretical model that encompasses the combined risk factors of long term absenteeism, the ability to develop, dissect and evaluate effective rehabilitation interventions is limited and replication of results becomes less likely. Identifying and modelling the relationship between these factors will prove valuable in informing the development of rehabilitation and support services for ‘at risk’ sufferers of workplace injury and reduce potentially long term absenteeism rates for employers. In consideration of a theoretical model of predictors of return to work, Lilley, Davie, Ameratunga and Derrett [10] assessed a combination of injury, demographic and psychosocial predictors of return to work and anecdotally suggested that the results should be further validated and applied to Engel’s [3] Biopsychosocial model (BPS) of health. According to this model, biological, psychological and psychosocial factors are represented in an equilateral ven diagram with the centre overlapping interaction indicating the cumulative overall health of an individual. However, Lilley et al. [10] and Mills [11] both acknowledge that researchers have intuitively agreed that injury factors such as severity and location of the injury will play a larger role in predicting poor return to work outcomes. Thus, while the existing biopsychosocial model may be used to guide future modelling of predictors of return to work, the equal variance, unquantifiable effect of individual variables, and absence of injury factors allows limited application of the model to real world rehabilitation prediction outcomes.
In addition, a recent study by Lainse, Lecomte and Corbiere [8] on biopsychosocial predictors in patients with musculoskeletal injuries found that psychosocial variables such as the importance of work to the individual and work support were predominant in predicting return to work outcomes. Although the article did not propose a specific model of the interaction between known predictive variables, it did significantly advance the understanding and importance of biopsychosocial factors by highlighting the importance of lesser measured variables such as post traumatic stress disorder (PTSD) symptomology and perceived importance of work. This finding further confirms the limited understanding in the present literature and the need for consolidation and validation of existing results. It is important to understand the factors associated with predicting long term absenteeism and non return to employment as a result of workplace injury in order to allow individuals at high risk to be identified and offered proactive rehabilitation programs and support services. Hence, the purpose of this systematic literature review was to identify predictors of poor return to work outcomes (as measured by long term absenteeism) in order to inform the future development of a holistic theoretical model and rehabilitation programs. The report details the process and findings of the review on demographic and psychosocial predictors of poor return to work outcomes following workplace injury. It further presents the current state of understanding of individual demographic and psychosocial characteristics associated with extended absenteeism. Herein, a return to work outcome was defined as resumption of a minimum of part time employment following a workplace injury related absenteeism of three months or greater.
2. Methods 2.1. Literature search A comprehensive literature search was conducted of scientific databases including Cochrane, EBSCOhost (CINAHL, Medline Complete, Humanities Source and PsychInfo), ProQuest and Science Direct using the terms: “predict* return to work” or “return to work” and “workplace injury”. The use of broad search terms was deliberately employed in order to identify all potential studies related to the subject matter. However, results were limited to publications entirely in English and which appeared in peer-reviewed journals between
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32 records identified through reference lists
6,389 titles excluded
485 abstracts screened
420 abstracts excluded
65 full-text articles assessed for eligibility
56 full-text articles excluded
9 studies included in qualitative synthesis
Screening
6,874 titles screened
Eligibility
6,842 records identified through databases
Included
Identification
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
Fig. 1. Summary procedure of the selection of studies and exclusion of studies based on predetermined inclusion criteria.
January 1990 and November 2012. Additional articles were sourced from the reference lists of the returned results and all potentially relevant studies were reviewed for suitability of inclusion within this study. The review focused on literature that included retrospective and longitudinal studies of injured workers. To be eligible for inclusion in the systematic literature review, a study must have satisfied a series of predefined criteria, namely: (a) tracked participants return to work status over a minimum of three months; (b) identified predictors of poor return to work outcomes; and (c) included a heterogeneous sample of workplace injuries. Studies that recorded only single injury cohorts such as brain or musculoskeletal injuries and only injury related predictors of return to work (e.g. pain ratings) were excluded due to their limited applicability to the general workforce. Brain and trauma injury studies were also excluded as the enduring nature of such injuries was considered likely to confound demographic and psychosocial predictors of return to work. Finally, studies were also excluded based on only including qualitative measures such as focus group data or subjective measures such as cessation of welfare payments. Although cessation of welfare payments has been interpreted by some authors as an indication of return to work status, the reviewers deemed such assumptions to be a potentially misleading measure of
return to work status as welfare payments may have been discontinued due to any number of reasons. The preliminary database search returned 6,842 results. An initial screening of the article titles resulted in 6,389 studies being excluded from the review. A review of abstracts of the remaining 453 articles resulted in an additional 420 studies being excluded. From the residual 33 studies, a search of their reference lists revealed an additional 32 titles that potentially met the selection criteria for inclusion. A full text evaluation of the final 65 studies resulted in 56 being excluded based on application of the defined selection criteria. The remaining nine studies were analysed and considered suitable for inclusion in this systematic review. Figure 1 illustrates the search and review processes involved in selecting studies. Each process was performed by two of four independent researchers and any discrepancies were resolved through discussion and review of the research question and selection criteria. 2.2. Methodological quality assessment The quality of each study in this analysis was assessed independently by two reviewers based on Hoesfsmit, Houkes and Nijkuis’ [6] modified version of the Cochrane review criteria with weighted scores (see Table 1). Final scores were determined by collaboration
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T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury Table 1 Criteria for evaluating the quality of empirical research
Criteria Design
Study population
Measurement Instruments
Data analysis
Outcomes
Evaluation Randomized Cluster randomised control trial trial (++) (++) 100 respondents (yes: ++ / no: - -) 2 times at least 3 months apart (++) Validity and reliability of instruments described or logically no description (yes: ++ / no: - -) Hierarchical re- Multiple regression gression or MANOVA (++) (+) Outcomes described (yes: ++ / no: - -)
Controlled trial or quasi-experiment (+)
Longitudinal Cross sectional Case/case study study series study (+) (-) (- -) Heterogeneous: respondents of different ages, gender and/or educational levels (yes: ++ / no: - -) 2 times at less than 3 months 1 data collection time (+) (-) Instruments are likely to be accepted in the profession under concern (yes: ++ / no: - -) Correlation, Survival analysis Logistical regres- Regression Chi-square or sion or Descrip- analysis or T-test ANOVA tive statistics (+) (-) (-) (-) Outcomes match study aims (yes: ++ / no: - -)
Note: - - = minus two, insufficient; - = minus one, sufficient; + = one, good; ++ = two, very good. A criterion is also ranked with a – where it could not be identified based on the text in the article. Table 2 Methodological quality of the included studies Study Cheadle et al. [2] He et al. [5] Kong et al. [7] Mills [11] Foreman et al. [4] Xu et al. [17] Li-Tsang et al. [9] Park [13] Tate [15]
Design + + + + + + + + +
Study population ++ ++ ++ ++ + + + ++ +
Follow up time ++ ++ ++ ++ ++ ++ -
Instruments ++ + + + + ++ ++ + +
Data analysis + + ++ + + + -
Outcomes ++ ++ ++ ++ ++ ++ ++ ++ ++
Rating Good Good Good Good Moderate Moderate Moderate Moderate Moderate
Note: Overall methodological quality rating of quantitative studies: - - (insufficient), - (poor), + (moderate), ++ (good).
of the independent reviews and were found to be uniform in the majority of cases. Table 2 outlines the reviewers’ final rating of the methodological quality of included studies. No studies were excluded based on the outcome of this process. Overall, the quantitative studies selected for review were assessed to have moderate to high quality methods associated with their design, study population, follow-up time, instruments and data analyses. Four studies [2,5,7,11] were considered to be of higher quality than the others due to their large, heterogeneous samples and sufficient follow-up time. In addition, these four studies generally employed superior data analyses techniques which added additional credibility to their findings. The quality assessment process undertaken also highlighted a number of limitations to existing research strategies. As noted by Cheadle et al. [2], Mills [11] and Tate [15], whilst the database extraction instrument used in all retrospective studies was rated as moder-
ate due to its general acceptance by the professional field, it provides limited value in assessing specific return to work related variables and may also contain undetectable administrative errors. Furthermore, the high rates of return to work outcomes observed by the reviewed studies (ranging from 50% [15] to 92% [5]) might also confound the assessment of predictors of poor return to work. 3. Results A total of 65 full journal articles were examined for eligibility and nine articles were included in the review. Appendix A illustrates the characteristics and predictors of poor return to work outcomes extracted from the included studies. 3.1. Study design and instruments Six of the nine studies [2,5,7,11,13,15] employed a retrospective cohort design whereby the researchers
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
extracted information from historical databases – including workers compensation claim records, employer records and medical files. Due to the limitations of retrospectively collected information, the majority of these studies examined only demographic and injury related predictors of return to work outcomes. The one exception was He et al. [5] who included a measure of psychosocial predictors of return to work based on primary sourced post-injury data collection. The remaining three studies [4,9,17] were prospective in nature, following participants longitudinally, and reported solely on psychosocial predictors of return to work. In these studies, the employed instruments of assessment varied greatly. While Foreman and Murphy [4] used semi-structured interviews based on expectancy theory, Xu et al. [17] employed a number of standard psychological measures and Li-Tsang et al. [9] used a combination of both (refer Appendix A). The number of participants in the included studies ranged from 32 to 28,473 and participant follow-up time also varied substantially from three months to in excess of three years. 3.2. Demographic predictors 3.2.1. Age Older age was found to be associated with poor return to work outcomes. Five of the studies [2,5,7,13, 15] in this review reported significant demographic predictors of poor return to work outcomes. All of these studies identified older age ( 46 years) as an important variable. It was aptly noted by Cheadle et al. [2] that this observed age effect was most likely due to both the reduced ability of older workers to recover from injury and the reduced likelihood of finding employment once they have recovered. 3.2.2. Gender Three studies assessed gender with two, namely Cheadle et al. [2] and Park [13], finding female gender was associated with poor return to work outcomes. In addition, a third study, Mills [11] reported an observed trend in female participants predicting poor return to work outcomes but was unable to show statistical significance for this finding. The authors of this literature review hypothesise that the traditional caregiver role of women in the home may impede injury recovery time, thus accounting for the lower return to work rates within this group. 3.2.3. Marital status Cheadle et al. [2] and He et al. [5] assessed marital status in connection with return to work outcomes.
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Although it was reported by Cheadle et al. [2] that divorced marital status was a stable predictor of poor return to work outcomes across the sample of 28,473 examined cases, the effect size of this finding was small. In contrast, He and colleagues [5] reported no significant correlation between marital status and poor return to work outcomes. However, it was noted that the limited sample size of this study (n = 323) may have influenced the lack of finding related to this variable. Commensurate with the conjecture that female gender is associated with poor return to work outcomes, it follows that divorcees with limited support in the home may similarly experience extended injury recovery times and therefore take longer to return to the workforce, if at all. 3.2.4. Dependent family members Of the nine studies that satisfied the criteria for inclusion, only He et al. [5] measured the association between number of dependent family members and return to work. It was reported that a higher number of dependents (n 2) was associated with a significantly longer duration of workforce absenteeism, but did not influence the final return to work outcome. In this case, the reviewers consider the definition of a higher number of dependent family members as being greater than or equal to two to reflect the cultural context of the Chinese society, from which participants were drawn, and may not be generally applicable across developed nations where higher numbers of dependents are common. 3.2.5. Education Four of the nine studies [5,7,13,15] found lower education level to be associated with poor return to work outcomes. Kong et al. [7] asserted that workers with high school or college education are 2.5 times more likely to return to work than those of lower education levels, whilst Park [13] surmised that injured workers with lower education levels may be less competitive in the labour market, especially where competition is high. This finding is also consistent with general education and employment trends across the Organisation for Economic Co-operation and Development (OECD) countries. For example, the OECD Employment Outlook 2012 [12] revealed that, in Australia, unemployment levels for individuals without an upper secondary education was 6.2% compared with 3.6% for those with an upper secondary school education and 2.8% with tertiary education. Thus, lower education level was found to be an important variable in predicting both general employment and poor return to work outcomes.
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3.2.6. Employment variables Employment variables were examined by three reviewed studies [2,5,15]. Within these reports, manual job descriptions [5], lower pre-injury wages and less time with employer [15], or employment in a firm size of less than 50 employees [2] were all found to be associated with poor return to work outcomes. In exploring these findings the reviewers consider it likely that manual job positions could, in comparison to white collar positions, result in injuries that demand greater recovery periods prior to returning to work. For example, injuries that occur in a manual occupation are likely to be of a higher physical severity and the associated level of functional fitness required to return to the job is also likely to be higher in most cases. In addition, these results are consistent with that of lower education levels (often associated with manual job descriptions) in predicting poor return to work outcomes. This finding may also be attributable to the job specific skill base in manual job descriptions resulting in limited transferability and a less competitive position when returning to the labour market. 3.3. Injury predictors Predictors of poor return to work outcomes related to the injury type and severity were reported by six of the nine studies [2,5,7,11,13,15]. The majority of these studies [5,7,13,15] reported higher injury severity as the most significant injury predictor of poor return to work outcomes. Furthermore, a specific diagnosis of: carpal tunnel syndrome [2]; back or neck injuries [2, 15] were also found to be predictive of adverse return to work outcomes. Mills [11] further suggested that the mechanism of injury (including lifting, muscular stress, sitting and repetitive movement) was also predictive of poor return to work outcomes. Thus, while this systematic literature review excluded studies that focused on a specific injury (such as brain and back injuries) as the findings could not be generalised across the workforce, it stands to reason that increased injury severity or sensitivity of the location of the injury would negatively influence recovery time and therefore be predictive of poor return to work outcomes. 3.4. Psychosocial predictors Of the five studies that assessed psychosocial predictors of poor return to work outcomes [4,5,7,9, 17] both Li-Tsang et al. [9] and Xu et al. [17] employed a number of standardised psychological mea-
sures and found only the C-LASER subscale of precontemplation (based on the Stages of Change theory) to be a significant predictor. This subscale assesses an individual’s readiness to change, and in the context of assessing return to work, resume employment. For example, Xu et al. [17] found that higher baseline scores on the C-LASER pre-contemplation subscale (reflecting reduced readiness to return to work) were significant in predicting continued absenteeism at the three month follow-up assessment. Similarly, it was reported that psychosocial measures of: poor valance and lower expected outcomes [4]; poor selfperceived health status and worrying about recurrence of injury [5]; and poor personal or family attitudes regarding return to work [7] predicted poor return to work outcomes. The common denominator between psychosocial predictors of poor return to work outcomes appears to be one of negative attitudes and perception prior to re-entry into the workforce. For example, the pre-contemplation Stage of Change represents an inability to acknowledge that dysfunctional behaviours are problematic. The other psychosocial predictors identified in this review mirror the notion of negative thought patterns and behaviours impeding return to work.
4. Discussion This systematic review examined predictors of poor return to work outcomes following workplace injury in general workplace injury populations. It was found that a number of demographic characteristics pertaining to older age, female gender, divorced marital status, two or more dependent family members and limited labour market competitiveness are predictive of poor return to work outcomes. In addition, injury and psychosocial predictor variables included injury severity, injury location and psychosocial assessments of negative attitudes and poor expectancy outcomes. Notably, the present review, and in particular the sporadic measurement of variables across studies, also highlights the lack of consensus in defining and measuring return to work outcomes. Comparison and assessment of the external validity of each predictive variable was further made difficult by the heterogeneity of study designs and narrow scope of retrospective data reviewed by most cases. For example, whereas Laisne and colleagues [8] supported the inclusion of: psychological factors such as symptoms of anxiety, depression and PTSD; and social factors such as work
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
importance and work support, in a model of predicting return to work outcomes, the homogeneous sample of musculoskeletal injuries raises the potential of limited application of the results across other injury categories and was therefore excluded from the present literature review. Regrettably, six of the nine studies reviewed in detail [2,5,7,11,13,15] relied solely on retrospective case file information not specifically tailored to assess return to work outcomes. Although this limitation is a reflection of practical difficulties faced by researchers in collecting data, especially as workplace injuries cannot be foreseen, it highlights a significant challenge in the field. As aptly noted by Young [18], failure to assess return to work outcomes in a manner that is both consistent and meaningful to stakeholders will result in diminished capacity to accurately develop rehabilitation interventions capable of influencing change. Hence, future research should involve a clear and consistent definition of return to work outcomes and the ability to tailor study designs to meet such definitions. The application of a holistic theoretical model would also enable more meaningful comparison of results and a clearer understanding and measurement of the value of predictor variables. Such improvements in research design would further enable more reliable and confident conclusions to be drawn by future reviewers. Finally, by improving the research methodology in this field, a greater understanding of the nature of factors that directly affect negative return to work outcomes may result in improved outcomes for the individual, employer and society as a whole. Specifically, greater understanding and consensus on an underpinning theoretical model in this area may facilitate early screening of injured workers in order to identify individuals with known risk factors associated with poor return to work outcomes. Moreover, rehabilitation programs and services could be tailored to address the needs of participants who exhibit such risk factors (e.g. older age, poor labour market competitiveness and low expectancy outcomes). It is therefore surmised that proactively identifying, theoretical modelling of, and providing additional tailored assistance to injured workers who are at a higher risk of poor recovery may enhance return to work outcomes.
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[3] [4]
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Appendix A. Description of studies and predictors of poor return to work Author year country Cheadle et al. [2] USA
Design study population
Instrument
Follow up time
RTW status
Predictors of poor RTW
Retrospective cohort study N = 28,473 Employees of a large stateowned locomotive company Women 55 years, Men 60 years
– Computerised claim data
Variable M = 35.4 months
17.5% 6 months NRTW 12% 1 year NRTW 7.5% 2 years NRTW
Demographic predictors: – – – – – –
Older age Female gender Divorced marital status Construction and agricultural work Firm size < 50 Higher county unemployment rates
Injury predictors: – Diagnosis of carpal tunnel or back/ neck sprain – Hospitalisation within the first 28 days – Semi-structured Foreman et Prospective cohort study interview based al. [4] N = 32 on expectancy Australia Federal public sector employees theory off work for at least 4 months Age not specified – Company He et al. [5] Retrospective cohort study archival China N = 323 documents Injured workers treated at Work Injury Rehabilitation Centre, Guangdong. Women 55 years, Men 60 years
6 months
7 months
34.4% RTW full time 34.4% RTW part time 31.2% NRTW
Psychosocial predictors:
92% RTW 8% NRTW
Demographic predictors:
– Poor valance and expectancy
– Older age – Lower education level – Higher number of dependent family members – Technical title – Monthly salary pre-injury – If the severity had been rated by the local authority Injury predictors: – Injury locus – Injury severity – Pain in the injury locus Psychosocial predictors: – Self-perceived health status – Worrying about re-injury or RTW – Satisfaction with pre-injury job
Kong et al. [7] China
– Company Retrospective cohort study archival N = 523 documents Injured workers treated at Work Injury Rehabilitation Centre, Guangdong. Women 55 years, Men 60 years
3–8 months
77.9% RTW 22.1% NRTW
Demographic predictors: – – – –
Older age Lower education level Low skill qualification No computer skill training
Injury predictors: – Higher injury severity Psychosocial predictors: – – – –
Li-Tsang et al. [9] Hong Kong
Prospective cohort study N = 75 From a local workers’ health centre who had previously participated in conventional rehabilitation services. Excluded severe brain and spinal injuries Cluster 1: C-LASER scores of Contemplation and Action Cluster 2: C-LASER scores of Pre-contemplation Age 20–65 years
– Interview – Spinal Function Sort (SFS) – Loma Linda University Medical Centre Activity Sort (LLUMC) – Chinese Lam’s Assessment of Stages of Employment Readiness (C-LASER) – Short Form 36 (SF-36)
T1: Baseline T2: Post training session T3: Post training and RTW program *Specific timeline unclear
T1: Cluster 1 65.2% RTW Cluster 2 48.1% RTW T2: Cluster 1 73.7% RTW Cluster 2 46.4% RTW T3: Cluster 1 57.4% RTW Cluster 2 42.9% RTW
Poor family attitude to RTW Poor personal desire to RTW No psychological counselling Non participation in disability adjustment activity
Psychosocial predictors: – LASER factor sub-scores of precontemplation
T.D. Street and S.J. Lacey / Predictors of poor return to work outcomes following workplace injury
Author year Design study population Instrument country – Worker’s comMills [11] Retrospective cohort study pensation claim Australia N = 9,048 records lodged Workers compensation claim 1 March to records 30 June, 2007 Age not specified Group A: n = 1,377 NSW Self insurer in the Retail sector Group B: n = 7,671 VIC State workers’ compensation authority (WorkSafe VIC) – Interview Park [13] Retrospective cohort study – Korean Workers’ South N = 13,078 Compensation Korea Injured workers registered in and 2005 as having a permanent disWelfare Services ability (KCOMWEL) Age 20–55 years insurance claim 90.1% male and telephone interview data – Korea Employment Information Services (KEIS) employment data Tate [15] USA
– Personal medical Retrospective cohort study files N = 200 Active working compensation claims January – June 1986 Blue collar workers at an automobile manufacturer, Michigan
Follow up time
RTW status
Predictors of poor RTW
3 months or until RTW
Group A: Days to RTW or 3 months Median = 25 days, SD = 55.5 days Group B: Days to RTW or 3 months Median = 76 days, SD = 52.8 days
Injury predictors:
37 months
47.7% RTW 18% RTW (new firm) 1.7% RTW (self employed) 32.2% NRTW
Demographic predictors:
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– Mechanisms of injury of lifting, muscular stress (no object), and posture / steeping / kneeling / sitting / repetitive movement
– Female gender – Older age – Lower education level Injury predictors: – High disability category
12 months
50% RTW 50% NRTW
Demographic predictors: – – – –
Older age Lower education Lower pre-injury wages Less years of employment at company
Injury predictors: – Higher injury severity – Back and neck injuries
Xu et al. [17] Hong Kong
– Chinese Lam’s Prospective cohort study 3 months post RTW 65.7% RTW Assessment of N = 67 program 34.3 NRTW Stages of Injured workers who particiEmployment pated in a 6 week RTW program Readiness Age 20–60 years (LASER) – VALPAR #19 (subset of Valpar Component Work Sample (VCWS)) – DEXTER Hand Evaluation System – Loma Linda University Medical Center (LLUMC) Activities Sort – Chinese version of the StateTrait Anxiety Inventory – Short Form 36 (SF-36)
Note. RTW = Return to work, NRTW = Non return to work.
Psychosocial predictors: – C-LASER factor sub-scores of precontemplation
